Dropout elimination & Conv Bugfix (#297)

* Dropout elimination.

* Test VGG19.

* Add shufflenet.

* Fix grouped convolution bug.

* Fix lit test failure.

src/Conversion/ONNXToKrnl/NN/Conv.cpp

@@ -135,8 +135,8 @@ struct ONNXConvOpLowering : public ConversionPattern {
                 /*mIndex=*/rewriter.getAffineDimExpr(1));
         kernel = rewriter.create<AffineApplyOp>(loc, kernelMap,
             ArrayRef<Value>{/*gIndex=*/outerLoops.getInductionVar(gIndex),
-                /*kernelsPerGroupValue=*/kernelsPerGroupValue,
+                /*mIndex=*/outerLoops.getInductionVar(mIndex),
-                /*mIndex=*/outerLoops.getInductionVar(mIndex)});
+                /*kernelsPerGroupValue=*/kernelsPerGroupValue});
       }
 
       // 2.2 Define spatial loops

src/Dialect/ONNX/ONNXOps.td.inc

@@ -1046,6 +1046,7 @@ def ONNXDivOp:ONNX_Op<"Div",
 
 def ONNXDropoutOp:ONNX_Op<"Dropout",
   [NoSideEffect, DeclareOpInterfaceMethods<ShapeInferenceOpInterface>]> {
+  let hasCanonicalizer = 1;
   let summary = "ONNX Dropout operation";
   let description = [{
   "Dropout takes one input floating tensor and produces two tensor outputs,"

src/Transform/ONNX/Combine.cpp

@@ -95,3 +95,9 @@ void ONNXTransposeOp::getCanonicalizationPatterns(
   result.insert<FuseTransposePattern>(context);
   result.insert<RemoveIdentityTransposePattern>(context);
 }
+
+/// on the ONNXDropoutOp.
+void ONNXDropoutOp::getCanonicalizationPatterns(
+    OwningRewritePatternList &result, MLIRContext *context) {
+  result.insert<DropoutEliminationPattern>(context);
+}

src/Transform/ONNX/Combine.td

@@ -24,6 +24,11 @@ include "src/Dialect/ONNX/ONNXOps.td"
 ///    dag benefitsAdded = (addBenefit 0)
 /// >;
 
+// Usefult code generation invokation.
+def GetNullAttr : NativeCodeCall<"Attribute()">;
+
+def GetUnitAttr: NativeCodeCall<"$_builder.getUnitAttr()">;
+
 def HasOneUse : Constraint<CPred<"$0.hasOneUse()">>;
 class HasRankOf<int rank> : Constraint<CPred<"$0.getType().isa<ShapedType>() && $0.getType().cast<ShapedType>().getRank() == " # rank>>;
 def HasNoneType : Constraint<CPred<"$0.getType().isa<NoneType>()">>;
@@ -55,11 +60,15 @@ def FuseGemmFollowedByAddition : Pat<(ONNXAddOp (ONNXGemmOp:$res $m1, $m2, $none
 def IdentityEliminationPattern : Pat<(ONNXIdentityOp $arg),
                                      (replaceWithValue $arg)>;
 
+// y, mask = onnx.Dropout(x) -> y, mask = x, none
+def DropoutEliminationPattern : Pattern<(ONNXDropoutOp $arg, $ratio),
+                                        [(replaceWithValue $arg),
+                                         (ONNXConstantOp (GetNullAttr), (GetUnitAttr))]>;
+
 def ConstantPadPattern : Pat<(ONNXPadConstantValueOp $m1, (ONNXConstantOp:$res $v1, $v2), $m2, $m3),
                              (ONNXPadConstantValuePadOp $m1, $v2, $m2, $m3),
                              [(HasOneUse $res)]>;
 
-
 // ONNX_Op (onnx.Cast (%X, $type)) = ONNX_Op (%X)
 def CastEliminationPattern : Pat<
 	(ONNXCastOp $arg, $type),

test/backend/test.py

@@ -415,8 +415,10 @@ test_to_enable = [
     "test_split_variable_parts_2d_cpu",
     "test_split_variable_parts_default_axis_cpu",
 
-    # ResNet
+    # Model
     "test_resnet50_cpu",
+    "test_vgg19_cpu",
+    "test_shufflenet_cpu",
 ]
 
 

test/mlir/onnx/onnx_lowering.mlir

@@ -1272,13 +1272,13 @@ func @test_conv_no_bias_no_pad_w_group(%arg0 : tensor<1x9x32x64xf32>, %arg1 : te
 
   // CHECK-LABEL: test_conv_no_bias_no_pad_w_group
   // CHECK: [[RES:%.+]] = alloc() : memref<1x5x27x58xf32>
-  // CHECK: [[CONST0:%.+]] = constant 1 : index
+  // CHECK: %[[CONST0:.+]] = constant 1 : index
   // CHECK: [[CONST1:%.+]] = constant 0.000000e+00 : f32
   // CHECK: [[CONST2:%.+]] = constant 3 : index
   // CHECK: [[OUTER_LOOPS:%.+]]:3 = krnl.define_loops 3
 
   // CHECK: krnl.iterate([[OUTER_LOOPS]]#0, [[OUTER_LOOPS]]#1, [[OUTER_LOOPS]]#2) with ([[OUTER_LOOPS]]#0 -> %arg2 = 0 to 1, [[OUTER_LOOPS]]#1 -> %arg3 = 0 to 3, [[OUTER_LOOPS]]#2 -> %arg4 = 0 to 1) {
-  // CHECK: %[[ADD1:.+]] = affine.apply #{{.*}}(%arg3, [[CONST0]])[%arg4]
+  // CHECK: %[[ADD1:.+]] = affine.apply #{{.*}}(%arg3, %arg4)[%[[CONST0]]]
   // CHECK: [[SPATIAL_LOOPS:%.+]]:2 = krnl.define_loops 2
 
   // CHECK: krnl.iterate([[SPATIAL_LOOPS]]#0, [[SPATIAL_LOOPS]]#1) with ([[SPATIAL_LOOPS]]#0 -> %arg5 = 0 to 27, [[SPATIAL_LOOPS]]#1 -> %arg6 = 0 to 58) {

utils/gen_onnx_mlir.py

@@ -321,7 +321,7 @@ OpsWithShapeInference=[
 ]
 
 # Operations supporting canonicalization.
-OpsWithCanonicalizer = ['Add', 'Identity', 'Gemm', 'Conv', 'Cast', 'Transpose']
+OpsWithCanonicalizer = ['Add', 'Identity', 'Gemm', 'Conv', 'Cast', 'Transpose', 'Dropout']
 
 # Operations who have operands that, if produced by constant operations, should
 # be promoted to become an attribute (via attribute promotion).